Identifying the same person across two similar social networks in a unified way: Globally and locally

With the increasing popularity of online social networks, people today typically own several such accounts (e.g., Facebook, Twitter, and Flickr). In this scenario, it arises an interesting and challenging problem: how to identify the same person in different social networks, which is known as the network reconciliation problem. Prior work on this problem first assumes that the relationships between users are homogeneous, and then makes use of the local features (degree, common mapped neighbors) to achieve high precision. However, this assumption does not hold in reality since users usually have different tie strengths between each other. In this paper, we remodel the reconciliation problem by considering the users' heterogeneous relationships and propose a unified framework called UniRank for incorporating the local features and global features together. Based on UniRank, we design an efficient two-stage network reconciliation algorithm. First, we design a global matching algorithm to explore more seeds with fast speed. Second, for each explored seed, we design a breadth-first strategy based local matching algorithm to match more seeds. Extensive simulations on real-world and synthetic social network datasets show that our algorithm significantly improves the state-of-art algorithm by up to 9X in terms of F1 score even under very rough conditions.